Global Optimization for Neural Network Training
Computer - Special issue: neural computing: companion issue to Spring 1996 IEEE Computational Science & Engineering
Machine Learning
Logging RAID - An Approach to Fast, Reliable, and Low-Cost Disk Arrays
Euro-Par '00 Proceedings from the 6th International Euro-Par Conference on Parallel Processing
Proceedings of the twentieth ACM symposium on Operating systems principles
Quickly finding near-optimal storage designs
ACM Transactions on Computer Systems (TOCS)
Modeling the relative fitness of storage
Proceedings of the 2007 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Using Black-Box Modeling Techniques for Modern Disk Drives Service Time Simulation
ANSS-41 '08 Proceedings of the 41st Annual Simulation Symposium (anss-41 2008)
Function approximation using artificial neural networks
WSEAS Transactions on Mathematics
A Performance Model of Zoned Disk Drives with I/O Request Reordering
QEST '09 Proceedings of the 2009 Sixth International Conference on the Quantitative Evaluation of Systems
Extract and infer quickly: Obtaining sector geometry of modern hard disk drives
ACM Transactions on Storage (TOS)
Storage device performance prediction with selective bagging classification and regression tree
NPC'10 Proceedings of the 2010 IFIP international conference on Network and parallel computing
Multilayer discrete-time neural-net controller with guaranteed performance
IEEE Transactions on Neural Networks
Smooth function approximation using neural networks
IEEE Transactions on Neural Networks
Training feedforward networks with the Marquardt algorithm
IEEE Transactions on Neural Networks
Storage Device Performance Prediction with Hybrid Regression Models
PDCAT '12 Proceedings of the 2012 13th International Conference on Parallel and Distributed Computing, Applications and Technologies
Hi-index | 0.00 |
Predicting access times is a crucial part of predicting hard disk drive performance. Existing approaches use white-box modeling and require intimate knowledge of the internal layout of the drive, which can take months to extract. Automatically learning this behavior is a much more desirable approach, requiring less expert knowledge, fewer assumptions, and less time. Others have created behavioral models of hard disk drive performance, but none have shown low per-request errors. A barrier to machine learning of access times has been the existence of periodic behavior with high, unknown frequencies. We show how hard disk drive access times can be predicted to within 0:83 ms using a neural net after these frequencies are found using Fourier analysis.